diff --git a/pcdet/datasets/processor/point_feature_encoder.py b/pcdet/datasets/processor/point_feature_encoder.py
new file mode 100644
index 0000000..d22bce9
--- /dev/null
+++ b/pcdet/datasets/processor/point_feature_encoder.py
@@ -0,0 +1,57 @@
+import numpy as np
+
+
+class PointFeatureEncoder(object):
+    def __init__(self, config, point_cloud_range=None):
+        super().__init__()
+        self.point_encoding_config = config
+        assert list(self.point_encoding_config.src_feature_list[0:3]) == ['x', 'y', 'z']
+        self.used_feature_list = self.point_encoding_config.used_feature_list
+        self.src_feature_list = self.point_encoding_config.src_feature_list
+        self.point_cloud_range = point_cloud_range
+
+    @property
+    def num_point_features(self):
+        return getattr(self, self.point_encoding_config.encoding_type)(points=None)
+
+    def forward(self, data_dict):
+        """
+        Args:
+            data_dict:
+                points: (N, 3 + C_in)
+                ...
+        Returns:
+            data_dict:
+                points: (N, 3 + C_out),
+                use_lead_xyz: whether to use xyz as point-wise features
+                ...
+        """
+        data_dict['points'], use_lead_xyz = getattr(self, self.point_encoding_config.encoding_type)(
+            data_dict['points']
+        )
+        data_dict['use_lead_xyz'] = use_lead_xyz
+       
+        if self.point_encoding_config.get('filter_sweeps', False) and 'timestamp' in self.src_feature_list:
+            max_sweeps = self.point_encoding_config.max_sweeps
+            idx = self.src_feature_list.index('timestamp')
+            dt = np.round(data_dict['points'][:, idx], 2)
+            max_dt = sorted(np.unique(dt))[min(len(np.unique(dt))-1, max_sweeps-1)]
+            data_dict['points'] = data_dict['points'][dt <= max_dt]
+        
+        return data_dict
+
+    def absolute_coordinates_encoding(self, points=None):
+        if points is None:
+            num_output_features = len(self.used_feature_list)
+            return num_output_features
+
+        assert points.shape[-1] == len(self.src_feature_list)
+        point_feature_list = [points[:, 0:3]]
+        for x in self.used_feature_list:
+            if x in ['x', 'y', 'z']:
+                continue
+            idx = self.src_feature_list.index(x)
+            point_feature_list.append(points[:, idx:idx+1])
+        point_features = np.concatenate(point_feature_list, axis=1)
+        
+        return point_features, True